Abstract：For high aggregation networks, we proposed a new method KEC for identifying important nodes in complex networks, which considered both the local information of nodes and neighbors, that is, the influence factors, and the contribution degree of neighbors to the influence of nodes, and put forward the contribution factor. In eight real networks, the method used the SIR model and deliberate attack experiments to analyze the performance of KEC and six commonly used centrality. Finally, the method used the Kendall-tau correlation coefficient to analyze the correlation between the values of nodes calculated by KEC and six commonly used centrality. The results show that it is effective for KEC to identify influential node sets and improve the destruction resistance of networks, and the Kendall-tau correlation coefficients between KEC and six commonly used centrality are almost positively correlated in eight real networks, which shows that it is feasible for KEC to identify important nodes in complex networks.
孙文静, 余路粉, 潘文林, 蓝春江. 基于节点影响因子和贡献因子的复杂网络重要节点识别[J]. 复杂系统与复杂性科学, 2026, 23(1): 87-95.
SUN Wenjing, YU Lufen, PAN Wenlin, LAN Chunjiang. Identification of Important Nodes in Complex Networks Based on Node Influence Factor and Contribution Factor[J]. Complex Systems and Complexity Science, 2026, 23(1): 87-95.
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